Brain signal analysis has revolutionized the research on human-computer interaction. Analyzing brain activity of the human emotions opens greater avenues to advance the research on Brain signal analysis. Human emotions play a significant role in social intercourse, human cognition, and decision making.[1] In this project, Differential Entropy (DE) features of EEG are used to perform emotion classification. The DE features are more suited for emotion recognition than Energy spectrum (ES) features which are used traditionally [2]. We have applied machine learning algorithms to discriminate three categories of human emotion: 1) positive 2) neutral and 3) negative. Feature extraction and dimensionality reduction are performed on the EEG dataset to obtain high-level features which helped to increase the accuracy and efficiency of the classification models. We have performed numerous machine learning models on the EEG data and compared the results of deep learning models and shallow models. .